AI EX 포트폴리오

지능화 캡스톤 - 프로젝트 1. WDI-CNN
-> Wafer Map 데이터를 9개의 클래스로 분류하는 CNN 모델

You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

Go to file

pinb 9b4f8ac3d4 업데이트 'readme.md'		2 years ago
CNN_Wafer.ipynb	init	2 years ago
readme.md	업데이트 'readme.md'	2 years ago

readme.md

지능화 캡스톤 프로젝트 #1 - WDI-CNN

Wafer Map 데이터를 9종류의 Class로 분류하는 CNN 모델 만들기

논문

반도체 제조공정의 불균형 데이터셋에 대한 웨이퍼 불량 식별을 위한 심층 컨볼루션 신경망

Dataset

Kaggle - WDI Data Pickle Dataset BMP Dataset

수행방법

위 논문을 참고하여 CNN 모델을 구현하고, WDI Dataset을 학습하여 9개의 클래스로 분류한다.

(Center, Donut, Edge-Loc, Edge-Ring, Loc, Near-full, none, Random, Scratch)

https://gitea.pinblog.codes/CBNU/03_WDI_CNN/releases/tag/info

Model

import torch
import torch.nn as nn
import torch.nn.functional as F

class CNN_WDI(nn.Module):
    def __init__(self, class_num=9):
        super(CNN_WDI, self).__init__()

        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=0)
        self.bn1 = nn.BatchNorm2d(16)
        self.pool1 = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(16, 16, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(16)

        self.conv3 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(32)
        self.pool2 = nn.MaxPool2d(2, 2)
        self.conv4 = nn.Conv2d(32, 32, kernel_size=3, padding=1)
        self.bn4 = nn.BatchNorm2d(32)

        self.conv5 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.bn5 = nn.BatchNorm2d(64)
        self.pool3 = nn.MaxPool2d(2, 2)
        self.conv6 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.bn6 = nn.BatchNorm2d(64)

        self.conv7 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.bn7 = nn.BatchNorm2d(128)
        self.pool4 = nn.MaxPool2d(2, 2)
        self.conv8 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.bn8 = nn.BatchNorm2d(128)

        self.spatial_dropout = nn.Dropout2d(0.2)
        self.pool5 = nn.MaxPool2d(2, 2)

        self.fc1 = nn.Linear(4608, 512)
        self.fc2 = nn.Linear(512, class_num)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.pool1(F.relu(self.bn2(self.conv2(x))))

        x = F.relu(self.bn3(self.conv3(x)))
        x = self.pool2(F.relu(self.bn4(self.conv4(x))))

        x = F.relu(self.bn5(self.conv5(x)))
        x = self.pool3(F.relu(self.bn6(self.conv6(x))))

        x = F.relu(self.bn7(self.conv7(x)))
        x = self.pool4(F.relu(self.bn8(self.conv8(x))))

        x = self.spatial_dropout(x)
        x = self.pool5(x)

        x = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)

        return F.softmax(x, dim=1)

cnn_wdi = CNN_WDI(class_num=9)

Load Data

from torchvision import transforms, datasets

# 데이터 전처리
rotation_angles = list(range(0, 361, 15))
rotation_transforms = [transforms.RandomRotation(degrees=(angle, angle), expand=False, center=None, fill=None) for angle in rotation_angles]

data_transforms = transforms.Compose([
    transforms.Pad(padding=224, fill=0, padding_mode='constant'),
    transforms.RandomHorizontalFlip(),
    transforms.RandomVerticalFlip(),
    transforms.RandomApply(rotation_transforms, p=1),
    transforms.CenterCrop((224, 224)),
    transforms.ToTensor(),
])

# ImageFolder를 사용하여 데이터셋 불러오기
train_dataset = datasets.ImageFolder(root='E:/wm_images/train/', transform=data_transforms)
val_dataset = datasets.ImageFolder(root='E:/wm_images/val/', transform=data_transforms)
test_dataset = datasets.ImageFolder(root='E:/wm_images/test/', transform=data_transforms)

Settings

import torch.optim as optim

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cnn_wdi.to(device)
print(str(device) + ' loaded.')

# 손실 함수 및 최적화 알고리즘 설정
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(cnn_wdi.parameters(), lr=0.001)

# 배치사이즈
batch_size = 18063360 #112

# 학습 및 평가 실행
num_epochs = 100 #* 192
# num_epochs = 50

# Random sample size
train_max_images = 95
val_max_images = 25

Train Function

# 학습 함수 정의
def train(model, dataloader, criterion, optimizer, device):
    model.train()
    running_loss = 0.0
    running_corrects = 0

    for inputs, labels in dataloader:
        inputs = inputs.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()

        outputs = model(inputs)
        _, preds = torch.max(outputs, 1)
        loss = criterion(outputs, labels)

        loss.backward()
        optimizer.step()

        running_loss += loss.item() * inputs.size(0)
        running_corrects += torch.sum(preds == labels.data)

    epoch_loss = running_loss / len(dataloader.dataset)
    epoch_acc = running_corrects.double() / len(dataloader.dataset)

    return epoch_loss, epoch_acc

Evaluate Function

# 평가 함수 정의
def evaluate(model, dataloader, criterion, device):
    model.eval()
    running_loss = 0.0
    running_corrects = 0

    with torch.no_grad():
        for inputs, labels in dataloader:
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)
            loss = criterion(outputs, labels)

            running_loss += loss.item() * inputs.size(0)
            running_corrects += torch.sum(preds == labels.data)

        epoch_loss = running_loss / len(dataloader.dataset)
        epoch_acc = running_corrects.double() / len(dataloader.dataset)

    return epoch_loss, epoch_acc

Train

# Train & Validation의 Loss, Acc 기록 파일
s_title = 'Epoch,\tTrain Loss,\tTrain Acc,\tVal Loss,\tVal Acc\n'
with open('output.txt', 'a') as file:
    file.write(s_title)
print(s_title)

for epoch in range(num_epochs + 1):
    # 무작위 샘플 추출
    train_indices = torch.randperm(len(train_dataset))[:train_max_images]
    train_random_subset = torch.utils.data.Subset(train_dataset, train_indices)
    train_loader = torch.utils.data.DataLoader(train_random_subset, batch_size=batch_size, shuffle=True, num_workers=4)
    
    val_indices = torch.randperm(len(val_dataset))[:val_max_images]
    val_random_subset = torch.utils.data.Subset(train_dataset, val_indices)
    val_loader = torch.utils.data.DataLoader(val_random_subset, batch_size=batch_size, shuffle=False, num_workers=4)

    # 학습 및 Validation 평가
    train_loss, train_acc = train(cnn_wdi, train_loader, criterion, optimizer, device)
    val_loss, val_acc = evaluate(cnn_wdi, val_loader, criterion, device)

    # 로그 기록
    s_output = f'{epoch + 1}/{num_epochs},\t{train_loss:.4f},\t{train_acc:.4f},\t{val_loss:.4f},\t{val_acc:.4f}\n'
    with open('output.txt', 'a') as file:
        file.write(s_output)
    print(s_output)

    if epoch % 10 == 0:
        # 모델 저장
        torch.save(cnn_wdi.state_dict(), 'CNN_WDI_' + str(epoch) + 'epoch.pth')

평가방법

모델의 성능지표(Precision, Recall, Accuracy, F1-Score)를 혼동행렬(Confusion Metrix)로 구현한다.

Confusion Metrix

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import classification_report, confusion_matrix
import pandas as pd

def plot_metrics(title, class_names, precisions, recalls, f1_scores, acc):
    num_classes = len(class_names)
    index = np.arange(num_classes)
    bar_width = 0.2

    plt.figure(figsize=(15, 7))
    plt.bar(index, precisions, bar_width, label='Precision')
    plt.bar(index + bar_width, recalls, bar_width, label='Recall')
    plt.bar(index + 2 * bar_width, f1_scores, bar_width, label='F1-score')
    plt.axhline(y=acc, color='r', linestyle='--', label='Accuracy')

    plt.xlabel('Class')
    plt.ylabel('Scores')
    plt.title(title + ': Precision, Recall, F1-score, and Accuracy per Class')
    plt.xticks(index + bar_width, class_names)
    plt.legend(loc='upper right')
    plt.show()

def predict_and_plot_metrics(title, model, dataloader, criterion, device):
    model.eval()
    running_loss = 0.0
    running_corrects = 0

    all_preds = []
    all_labels = []
    class_names = ['Center', 'Donut', 'Edge-Loc', 'Edge-Ring', 'Loc', 'Near-full', 'none', 'Random', 'Scratch']

    with torch.no_grad():
        for inputs, labels in dataloader:
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)
            loss = criterion(outputs, labels)

            running_loss += loss.item() * inputs.size(0)
            running_corrects += torch.sum(preds == labels.data)

            all_preds.extend(preds.cpu().numpy())
            all_labels.extend(labels.cpu().numpy())

        epoch_loss = running_loss / len(dataloader.dataset)
        epoch_acc = running_corrects.double() / len(dataloader.dataset)


    # Calculate classification report
    report = classification_report(all_labels, all_preds, target_names=class_names, output_dict=True)

    # Calculate confusion matrix
    cm = confusion_matrix(all_labels, all_preds)

    # Calculate precision, recall, and f1-score per class
    precisions = [report[c]['precision'] for c in class_names]
    recalls = [report[c]['recall'] for c in class_names]
    f1_scores = [report[c]['f1-score'] for c in class_names]
    print('p: ' + str(precisions))
    print('r: ' + str(recalls))
    print('f: ' + str(f1_scores))

    # Plot confusion matrix with normalized values (percentage)
    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
    plt.figure(figsize=(12, 12))
    sns.heatmap(cm_normalized, annot=True, fmt='.2%', cmap='Blues', xticklabels=class_names, yticklabels=class_names)
    plt.xlabel('Predicted Label')
    plt.ylabel('True Label')
    plt.title('Normalized Confusion Matrix: ' + title)
    plt.show()

    # Plot precision, recall, f1-score, and accuracy per class
    plot_metrics(title, class_names, precisions, recalls, f1_scores, epoch_acc.item())

    return epoch_loss, epoch_acc, report

Evaluate

import os
import re

test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=112, shuffle=False, num_workers=4)

dir = '.'
models = [file for file in os.listdir(dir) if file.endswith(('.pth'))]

def extract_number(filename):
    return int(re.search(r'\d+', filename).group(0))

sorted_models = sorted(models, key=extract_number)

for model in sorted_models:
    model_path = os.path.join(dir, model)

    # Load the saved model weights
    cnn_wdi.load_state_dict(torch.load(model_path))

    # Call the predict_and_plot_metrics function with the appropriate arguments
    epoch_loss, epoch_acc, report = predict_and_plot_metrics(model, cnn_wdi, test_loader, criterion, device)
    # print(f'Model: {model} Test Loss: {test_loss:.4f} Acc: {test_acc:.4f}')

Loss Graph

import matplotlib.pyplot as plt

# 파일에서 데이터를 읽어들입니다.
with open('output.txt', 'r') as file:
    lines = file.readlines()[1:]  # 첫 번째 줄은 헤더이므로 건너뜁니다.

# 데이터를 분석하여 리스트에 저장합니다.
epochs = []
train_losses = []
train_accuracies = []
val_losses = []
val_accuracies = []

for line in lines:
    if line == '\n':
        continue
    # epoch, train_loss, train_acc, val_loss, val_acc = line.strip().split(', \t')
    epoch, train_loss, train_acc, val_loss, val_acc = re.split(r'[,\s\t]+', line.strip())
    epochs.append(int(epoch.split('/')[0]))
    train_losses.append(float(train_loss))
    train_accuracies.append(float(train_acc))
    val_losses.append(float(val_loss))
    val_accuracies.append(float(val_acc))

# 선 그래프를 그립니다.
plt.figure(figsize=(10, 5))

plt.plot(epochs, train_losses, label='Train Loss')
plt.plot(epochs, train_accuracies, label='Train Acc')
plt.plot(epochs, val_losses, label='Val Loss')
plt.plot(epochs, val_accuracies, label='Val Acc')

plt.xlabel('Epochs')
plt.ylabel('Values')
plt.title('Training and Validation Loss and Accuracy')
plt.legend()
plt.show()

Print Selecting Test Model Result

def output(model, dataloader, criterion, device):
    model.eval()
    running_loss = 0.0
    running_corrects = 0

    all_preds = []
    all_labels = []
    class_names = ['Center', 'Donut', 'Edge-Loc', 'Edge-Ring', 'Loc', 'Near-full', 'none', 'Random', 'Scratch']

    with torch.no_grad():
        for inputs, labels in dataloader:
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)
            loss = criterion(outputs, labels)

            running_loss += loss.item() * inputs.size(0)
            running_corrects += torch.sum(preds == labels.data)

            all_preds.extend(preds.cpu().numpy())
            all_labels.extend(labels.cpu().numpy())

        epoch_loss = running_loss / len(dataloader.dataset)
        epoch_acc = running_corrects.double() / len(dataloader.dataset)


    # Calculate classification report
    report = classification_report(all_labels, all_preds, target_names=class_names, output_dict=True)

    # Calculate precision, recall, and f1-score per class
    precisions = [report[c]['precision'] for c in class_names]
    recalls = [report[c]['recall'] for c in class_names]
    f1_scores = [report[c]['f1-score'] for c in class_names]
    accuracy = report['accuracy']

    precs = sum(precisions) / len(precisions)
    recs = sum(recalls) / len(recalls)
    f1s = sum(f1_scores) / len(f1_scores)
    print('precisions: ' + str(precs))
    print('recalls: ' + str(recs))
    print('f1_scores: ' + str(f1s))
    print('accuracy ' + str(accuracy))


selected_model = 'CNN_WDI_20epoch.pth'
cnn_wdi.load_state_dict(torch.load(selected_model))
output(cnn_wdi, test_loader, criterion, device)

테스트 결과

1차 테스트